Continuous Transition: Improving Sample Efficiency for Continuous Control Problems via MixUp

Junfan Lin
Junfan Lin
Zhongzhan Huang
Zhongzhan Huang
Weiwei Chen
Weiwei Chen
Cited by: 0|Bibtex|Views29
Other Links: arxiv.org

Abstract:

Although deep reinforcement learning~(RL) has been successfully applied to a variety of robotic control tasks, it's still challenging to apply it to real-world tasks, due to the poor sample efficiency. Attempting to overcome this shortcoming, several works focus on reusing the collected trajectory data during the training by decomposing...More

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